A comparative study of fuzzy linear regression and multiple linear regression in agricultural studies: a case study of lentil yield management

This study investigates the advantages of two fuzzy linear regression (FLR) models, namely the Tanaka and the Savic and Pedrycz models, over multiple linear regression (MLR) for lentil yield management. We used a fuzzy approach to model the yield of lentil genotypes in which the input is crisp and the output fuzzy. Moreover, after finding FLR equations, we estimated the output corresponding to the collection of fuzzy inputs by using fuzzy algebraic operations and an appropriate defuzzification method known as the center of area method. Results showed the superiority of the Tanaka model over MLR because of reducing the included variables, especially variables available after harvest. The study also emphasizes the advantage of the Savic and Pedrycz model in comparison to the other two models with a smaller error rate.